To ensure the safety and security of the consumers using LPG the study aims to develop a device that can detect gas, smoke, and flame from the LPG cylinder and can notify the owner via text messages applying a rule-based approach. LPG can make the lives of the people in the house endangered and can cause cold burns to the skin.
If the source of ignition and gas meets LPG can explode or burn. LPG cylinder may leak as a liquid or a gas. LPG is currently used by hundreds of millions of people in the world and there are more than 1000 applications. Liquefied Petroleum Gas (LPG) is readily available to consumers around the world because LPG is a clean, portable, and efficient source of energy. Extensive experiments have verified the efficiency of DarkCNN for smoke detection based on single frame. The classification results are used to localize the smoke region.
SLIC is employed to segment the image and generate smoke candidates for DarkCNN. In addition, to localize the smoke region within an image with low time cost, a pipeline combining Simple Linear Iterative Cluster (SLIC) algorithm and DarkCNN has been developed which has much lower computational complexity than methods like R-CNN and its variants. To address the imbalanced data distribution problem, weighted softmax loss was adopted to train the DarkCNN. The dark channel of an image could enhance the difference between the smoke and non-smoke pixels and thus provide more discriminant features. A Min pooling layer is designed and adopted next to the input layer to extract the dark channel feature of the image. To address the above issues, a dark channel boosted convolutional neural network has been developed for smoke detection termed as DarkCNN.
In addition, in contrary to the normal state forest images, it is much more difficult to collect early stage forest fire images, so the available training data for forest smoke detection is severely imbalanced. However, the appearance of smoke might share similarity with the background which makes accurate smoke detection difficult. Timely detection of forest fire in the early stage could effectively avoid possible disaster which requires efficient smoke recognition instead of fire identification.
Forest fire causes severe economic and ecological damage each year.
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